The challenge of a motion capture system is to begin with a two-dimensional (2D) video of an animate body, and from that image sequence, to provide three-dimensional (3D) kinematic data. In other words, the motion capture system transforms 2D appearance data into 3D kinematic data.
The animate subject of motion capture can be human, animal, or any other moving body. The applications of motion capture are numerous, and include medical rehabilitation, sports, and virtual reality.
In the past, markers such as reflectors or sensors, have been placed on the subject (typically a human) under camera observation so that correspondences can be matched from 2D to 3D. However, these and other applications are greatly facilitated if there is no need for markers.
Conventionally, markerless motion capture systems use the shape and morphology of the human body to imply a virtual array of markers. The result is a 3D model, which can be combined with algorithms that express how a specific subject moves and changes shape over time.
A limitation of conventional markerless motion capture systems, as compared to marker-based systems, is accuracy of the resulting model. Existing markerless motion capture systems tend to not achieve parity with marker-based systems.